The exposure, sensitivity and vulnerability of natural vegetation in China to climate thermal variability (1901-2013): An indicator-based approach

• Published in: Ecological Indicators
• Main Authors: Xu, Yue, Shen, Ze-Hao, Ying, Ling-Xiao, Ciais, Philippe, Liu, Hong-Yan, Piao, Shi-long, Wen, Cheng, Jiang, You-Xu
• Other Authors: Shen, ZH (reprint author), Peking Univ, Coll Urban & Environm Sci, MOE Key Lab Earth Surface Proc, Dept Ecol, Beijing 100871, Peoples R China., Peking Univ, Coll Urban & Environm Sci, MOE Key Lab Earth Surface Proc, Dept Ecol, Beijing 100871, Peoples R China., CE LOrme Merisiers, UMR CEA CNRS UVSQ, Lab Sci Climat & Environm, Batiment 709, F-91191 Gif Sur Yvette, France., Peking Univ, Sch Life Sci, Beijing 100871, Peoples R China., Chinese Acad Forestry, Inst Forest Ecol Environm & Protect, Beijing 100093, Peoples R China.
• Format: Journal/Newspaper
• Language: Chinese
• Published: ECOLOGICAL INDICATORS 2016
• Subjects: Thermal variability; Vegetation; Vulnerability; Exposure; Sensitivity; Resilience; LAND-USE CHANGE; BIODIVERSITY CONSERVATION; COMMUNITY RESILIENCE; PLANT-COMMUNITIES; PROTECTED AREAS; EXTREME EVENTS; CHANGE IMPACTS; ECOSYSTEM; FOREST; DIVERSITY; Tundra
• Online Access: https://hdl.handle.net/20.500.11897/437443; https://doi.org/10.1016/j.ecolind.2015.12.023

Vulnerability assessments can be helpful in assessing the impact of climate change on natural ecosystems and are expected to support adaptation and/or mitigation strategies in the 21st century. A challenge when conducting such assessments is the integration of the multi-level properties and processes of ecosystems into an assessment framework. Focusing on the primary stresses of climate thermal variability (at both upper and lower extremes), this study proposes a quantitative indicator system following the IPCC framework of vulnerability assessment that assesses the impact of historical climate change, during 1901-2013, on the natural terrestrial vegetation types in China. The final output of the vulnerability assessment was expressed as a composite index, composed of ecosystem exposure, sensitivity and resilience to climate thermal change, and including biological, ecological and spatial traits of vegetation types in the assessment. The exposure to temperature variability was generally higher in January than in July, and higher in non-arborous vegetation types than forests. In contrast, sensitivity was higher for forests, wetlands and alpine tundra regions, especially for small areas and areas with scattered patterns. Original forests especially those distributed in the north had lower resilience than other vegetation types. The vulnerability of natural vegetation types in China to the temperature variability of the past century was very low to moderate, with a few exceptions, including tropical mangroves and the semiarid to arid vegetation types in northwestern China, which had high vulnerability. Vulnerability was stronger in winter than in summer. Our results are generally in accord with the scenario-based projections on the geographical pattern of vegetation vulnerability to climate change, and revealed the difference caused by not considering moisture. The risks for these fragmented and narrow-range ecosystems are highlighted, and the importance of natural resilience is stressed for the assessment of vegetation vulnerability to climate change. Given the inadequate coverage of the natural reserve network in China (after the large investment in recent decades) found in the high-vulnerability vegetation types (with a few exceptions), the assessment of natural resilience of ecosystems could be critical for the optimal design of socio-economic strategies in response to the impacts of future climate change. (C) 2015 Elsevier Ltd. All rights reserved. National Natural Science Foundation of China [31170449, 41371190, 31321061]; international project of the Ministry of Environmental Protection on "The Development of Indicator Systems for the Interactions between Climate Change and Biodiversity in China" SCI(E) EI ARTICLE 258-272 63